CMPSCI 590D Algorithms for Data Science

Instructor: Barna Saha
Office: CS 336. Office phone: (413) 577-2510. E-mail: barna@cs.umass.edu
Instructor Office Hours: Tue 11:30-12:30pm in CS336

Instructor: David Wemhoener
E-mail: wem@cs.umass.edu
Instructor Office Hours: Thu 2-3pm in LGRC A343

Teaching Assistant: Sainyam Galhotra
E-mail: sainyam@cs.umass.edu
Office Hour: Wed 2-3pm in CS207

Teaching Assistant: Raghavendra Addanki
E-mail: raddanki@cs.umass.edu
Office Hour: Mon 4:30-5:30pm in CS207

Class Time: TuThu 10:00-11:15 am in ILCS 131
Piazza Link: We will use Piazza for all class related discussions. Sign up here.

Course Overview:

Big Data brings us to interesting times and promises to revolutionize our society from business to government, from healthcare to academia. As we walk through this digitized age of exploded data, there is an increasing demand to develop unified toolkits for data processing and analysis. In this course our main goal is to rigorously study the mathematical foundation of big data processing, develop algorithms and learn how to analyze them. Specific Topics to be covered include (subject to change):
  1. Clustering
  2. Estimating Statistical Properties of Data
  3. Near Neighbor Search
  4. Algorithms over Massive Graphs and Social Networks
  5. Learning Algorithms
  6. Randomized Algorithms

Course Details:

Text Book: We will use reference materials from the following books. Both can be downloaded for free.
Prerequisities: CMPSCI 311 and CMPSCI 240 or equivalent courses are required with grade of B or better in both the courses. Students require proper background in algorithm design and basic probability, and will not be admitted in the course without satisfying the prerequisities.

Requirements and Grading: There will be 4 homeworks/programming assignments which will count towards 30% of the grade. Additionally, there will be few simple mini-exercises (roughly 4) which will count towards 20% of the grade. All homeworks and mini-exercises can be done individually or as part of a group of size 2 or 3. There will be one midterm and one final with respective weights of 20% and 30%.

Late Homework Policy: No late submission is allowed unless there are compelling reasons and pre-approved by the instructor.

Feedback: Students can submit course feedback at anytime through this online form